Is interestedin the ethicalimplicationsof generativeAIHas apreferred AIresearch toolthey canrecommendCanrecommenda good AI ortech relatedpodcastHasexperiencewith fine-tuning a pre-trained LLMHas used anLLM tosummarizeresearchpapersHas experiencewith low-resourcelanguages inNLPHas used agenerative AImodel for anon-academicpurposeHasattended anICMLconferencebeforeHas collaboratedon a researchpaper withsomeone from adifferent continentIs currentlyworking on aprojectinvolving cross-lingual transferlearningHas used agenerative AImodel tocreate art ormusicIs excitedabout thepotential ofLLMs ineducationIs familiarwith theconcept ofpromptengineeringCan explain thedifferencebetween causaland maskedlanguagemodelsHassuccessfullydebugged acomplexLLMIs optimisticabout thefuture ofhuman-AIcollaborationHascontributedto an open-source AIprojectCan namethreedifferent LLMarchitecturesHaspublishedresearch onmultilingualLLMsHas learneda newlanguage inthe last yearHas traveledinternationallyto attend thisconferenceHasparticipated ina hackathonfocused on AIor LLMsHas presenteda paper onnaturallanguagegenerationKnows atleast threeprogramminglanguagesIs interestedin the ethicalimplicationsof generativeAIHas apreferred AIresearch toolthey canrecommendCanrecommenda good AI ortech relatedpodcastHasexperiencewith fine-tuning a pre-trained LLMHas used anLLM tosummarizeresearchpapersHas experiencewith low-resourcelanguages inNLPHas used agenerative AImodel for anon-academicpurposeHasattended anICMLconferencebeforeHas collaboratedon a researchpaper withsomeone from adifferent continentIs currentlyworking on aprojectinvolving cross-lingual transferlearningHas used agenerative AImodel tocreate art ormusicIs excitedabout thepotential ofLLMs ineducationIs familiarwith theconcept ofpromptengineeringCan explain thedifferencebetween causaland maskedlanguagemodelsHassuccessfullydebugged acomplexLLMIs optimisticabout thefuture ofhuman-AIcollaborationHascontributedto an open-source AIprojectCan namethreedifferent LLMarchitecturesHaspublishedresearch onmultilingualLLMsHas learneda newlanguage inthe last yearHas traveledinternationallyto attend thisconferenceHasparticipated ina hackathonfocused on AIor LLMsHas presenteda paper onnaturallanguagegenerationKnows atleast threeprogramminglanguages

Human BINGO: Navigating Generative AI and LLMs Across Languages - Call List

(Print) Use this randomly generated list as your call list when playing the game. There is no need to say the BINGO column name. Place some kind of mark (like an X, a checkmark, a dot, tally mark, etc) on each cell as you announce it, to keep track. You can also cut out each item, place them in a bag and pull words from the bag.


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  1. Is interested in the ethical implications of generative AI
  2. Has a preferred AI research tool they can recommend
  3. Can recommend a good AI or tech related podcast
  4. Has experience with fine-tuning a pre-trained LLM
  5. Has used an LLM to summarize research papers
  6. Has experience with low-resource languages in NLP
  7. Has used a generative AI model for a non-academic purpose
  8. Has attended an ICML conference before
  9. Has collaborated on a research paper with someone from a different continent
  10. Is currently working on a project involving cross-lingual transfer learning
  11. Has used a generative AI model to create art or music
  12. Is excited about the potential of LLMs in education
  13. Is familiar with the concept of prompt engineering
  14. Can explain the difference between causal and masked language models
  15. Has successfully debugged a complex LLM
  16. Is optimistic about the future of human-AI collaboration
  17. Has contributed to an open-source AI project
  18. Can name three different LLM architectures
  19. Has published research on multilingual LLMs
  20. Has learned a new language in the last year
  21. Has traveled internationally to attend this conference
  22. Has participated in a hackathon focused on AI or LLMs
  23. Has presented a paper on natural language generation
  24. Knows at least three programming languages